SNA Descritive Analysis from “Projeto Redes de Atenção às pessoas que consomem álcool e outras Drogas em Juiz de Fora-MG Brazil” - SNArRDJF
Here you can find a basic script to analysis data from SNArRDJF - this script was elaborated considering its use for orther matrix adjacency data from SNArRDJF - Here we are going to analyse:
########################## Basic Preparation ##### `#########################
rm(list = ls()) # removing previous objects to be sure that we don't have objects conflicts name
load("~/SNArRDJF/Robject/1_intro_x2.RData")
suppressMessages(library(RColorBrewer))
suppressMessages(library(car))
suppressMessages(library(xtable))
suppressMessages(library(igraph))
suppressMessages(library(miniCRAN))
suppressMessages(library(magrittr))
suppressMessages(library(keyplayer))
suppressMessages(library(dplyr))
suppressMessages(library(feather))
suppressMessages(library(visNetwork))
suppressMessages(library(knitr))
suppressMessages(library(DT))
#In order to get dinamic javascript object install those ones. If you get problems installing go to Stackoverflow.com and type your error to discover what to do. In some cases the libraries need to be intalled in outside R libs.
#devtools::install_github("wch/webshot")
#webshot::install_phantomjs()
set.seed(123)
x2<-simplify(x2) #Simplify
• For undirected graphs:
– Actor centrality - involvement (connections) with other actors
• For directed graphs:
– Actor centrality - source of the ties (outgoing edges)
– Actor prestige - recipient of many ties (incoming edges)
In general - high centrality degree means direct contact with many other actors
V(x2)$indegree<-degree(x2, mode = "in") # Actor prestige - recipient of many ties (incoming edges)
V(x2)$outdegree <- degree(x2, mode = "out") # Actor centrality - source of the ties (outgoing edges)
V(x2)$totaldegree <- degree(x2, mode = "total")
x2_indegree<-degree(x2, mode = "in")
x2_outdegree<-degree(x2, mode = "out")
x2_totaldegree<-degree(x2, mode = "total")
##in
summary(x2_indegree)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 3.00 5.00 8.31 9.00 106.00
sd(x2_indegree)
## [1] 10.84693
hist(degree(x2, mode = "in", normalized = F), ylab="Frequency", xlab="Degree", breaks=vcount(x2)/10, main="Histogram of Indegree Nodes - 1.2 - ALGUM RELACIONAMENTO (x2)")
##out
summary(x2_outdegree)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 1.00 4.00 8.31 8.00 105.00
sd(x2_outdegree)
## [1] 13.91758
hist(degree(x2, mode = "out", normalized = F), ylab="Frequency", xlab="Degree", breaks=vcount(x2)/10, main="Histogram of Outdegree Nodes - 1.2 - ALGUM RELACIONAMENTO (x2)")
##all
summary(x2_totaldegree)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 5.00 10.00 16.62 17.50 211.00
sd(x2_totaldegree)
## [1] 23.39286
hist(degree(x2, mode = "all", normalized = F), ylab="Frequency", xlab="Degree", breaks=vcount(x2)/10, main="Histogram of All Degree Nodes - 1.2 - ALGUM RELACIONAMENTO (x2)")
A slightly more nuanced metric is “strength centrality”, which is defined as the sum of the weights of all the connections for a given node. This is also sometimes called “weighted degree centrality”
V(x2)$x2_strength<- strength(x2, weights=E(x2)$weight)
x2_strength<- strength(x2, weights=E(x2)$weight)
summary(x2_strength)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 5.00 10.00 16.62 17.50 211.00
sd(x2_strength)
## [1] 23.39286
hist(strength(x2, weights=E(x2)$weight), ylab="Frequency", xlab="Degree", breaks=vcount(x2)/10, main="Histogram of Strength Degree Nodes - 1.2 - ALGUM RELACIONAMENTO (x2)")
V(x2)$indegree_n<-degree(x2, mode = "in", normalized = T)
V(x2)$outdegree_n<- degree(x2, mode = "out", normalized = T)
V(x2)$totaldegree_n<- degree(x2, mode = "total", normalized = T)
x2_indegree_n<-degree(x2, mode = "in", normalized = T)
x2_outdegree_n<-degree(x2, mode = "out", normalized = T)
x2_totaldegree_n<-degree(x2, mode = "total", normalized = T)
summary(x2_indegree_n)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.01613 0.02688 0.04468 0.04839 0.56990
sd(x2_indegree_n)
## [1] 0.05831685
hist(degree(x2, mode = "in", normalized = T), ylab="Frequency", xlab="Normalized Degree", breaks=vcount(x2)/10, main="Histogram of Normalized Indegree Nodes - 1.2 - ALGUM RELACIONAMENTO (x2)")
summary(x2_outdegree_n)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.005376 0.021510 0.044680 0.043010 0.564500
sd(x2_outdegree_n)
## [1] 0.07482569
hist(degree(x2, mode = "out", normalized = T), ylab="Frequency", xlab="Normalized Degree", breaks=vcount(x2)/10, main="Histogram of Normalized Outdegree Nodes - 1.2 - ALGUM RELACIONAMENTO (x2)")
summary(x2_totaldegree_n)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.005376 0.026880 0.053760 0.089360 0.094090 1.134000
sd(x2_totaldegree_n)
## [1] 0.1257681
hist(degree(x2, mode = "all", normalized = T), ylab="Frequency", xlab="Normalized Degree", breaks=vcount(x2)/10, main="Histogram of Normalized All Degree Nodes - 1.2 - ALGUM RELACIONAMENTO (x2)")
V(x2)$x2_centr_degree <- centralization.degree(x2)$res
x2_centr_degree <- centralization.degree(x2)
x2_centr_degree$centralization
## [1] 0.5253353
x2_centr_degree$theoretical_max
## [1] 69192
x2_degree.distribution<-degree.distribution(x2)
summary(x2_degree.distribution)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.000000 0.004717 0.000000 0.101600
sd(x2_degree.distribution)
## [1] 0.01479026
hist(degree.distribution(x2), breaks=vcount(x2)/10, ylab="Frequency", xlab="Degree Distribuition", main="Histogram of Degree Distribuition - 1.2 - ALGUM RELACIONAMENTO (x2)")
dd <- degree.distribution(x2, cumulative=T, mode="all")
plot(dd, pch=19, cex=1, col="orange", xlab="Degree", ylab="Cumulative Frequency", main= "Cumulative Frequency of 1.2 - ALGUM RELACIONAMENTO (x2) ")
dd.x2 <- degree.distribution(x2)
d <- 1:max(degree(x2))-1
ind <- (dd.x2 != 0)
plot(d[ind],
dd.x2[ind],
log="xy",
col="blue",
xlab=c("Log-Degree"),
ylab=c("Log-Intensity"),
main="Log-Log Degree Distribution For 1.2 - ALGUM RELACIONAMENTO (x2)"
)
The neighborhood of a given order y of a vertex v includes all vertices which are closer to v than the order. Ie. order y=0 is always v itself, order 1 is v plus its immediate neighbors, order 2 is order 1 plus the immediate neighbors of the vertices in order 1, etc.
x2_a.nn.deg <- graph.knn(x2, weights =E(x2)$weight)$knn %>% round(1)
V(x2)$x2_a.nn.deg <- graph.knn(x2, weights=E(x2)$weight)$knn
d<-cbind(V(x2)$LABEL_COR,x2_a.nn.deg)
datatable(d)
plot(degree(x2),
x2_a.nn.deg,
log="xy",
col="goldenrod",
xlab=c("Log Vertex Degree"),
ylab=c("Log Average Neighbor Degree"),
main="Average Neighbor Degree vs Vertex Degree - Log-Log Scale for 1.2 - ALGUM RELACIONAMENTO (x2)"
)
x2_a.nn.deg_w <- graph.knn(x2,V(x2), weights=E(x2)$weight)$knn %>% round(1)
V(x2)$x2_a.nn.deg_w <-x2_a.nn.deg <- graph.knn(x2,V(x2), weights=E(x2)$weight)$knn
summary(x2_a.nn.deg_w)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.00 36.30 52.50 62.32 86.20 211.00
sd(x2_a.nn.deg_w, na.rm = T)
## [1] 36.79456
d<-cbind(V(x2)$LABEL_COR,x2_a.nn.deg_w)
datatable(d)
plot(degree(x2),
x2_a.nn.deg,
log="xy",
col="goldenrod",
xlab=c("Log Vertex Degree"),
ylab=c("Log Average Neighbor Degree"),
main="Average Weighted Neighbor Degree vs Vertex Degree - Log-Log Scale For Weighted 1.2 - ALGUM RELACIONAMENTO (x2)"
)
x2_indegree<-degree(x2, mode = "in")
x2_outdegree<-degree(x2, mode = "out")
x2_totaldegree<-degree(x2, mode = "total")
x2_strength<- strength(x2, weights=E(x2)$weight)
x2_indegree_n<-degree(x2, mode = "in", normalized = T) %>% round(3)
x2_outdegree_n<-degree(x2, mode = "out", normalized = T) %>% round(3)
x2_totaldegree_n<-degree(x2, mode = "total", normalized = T) %>% round(3)
x2_centr_degree <- centralization.degree(x2)$res
x2_a.nn.deg <- graph.knn(x2,V(x2))$knn %>% round(1)
x2_a.nn.deg_w <- graph.knn(x2,V(x2), weights=E(x2)$weight)$knn %>% round(1)
x2_df_degree <- data.frame(x2_indegree,
x2_outdegree,
x2_totaldegree,
x2_indegree_n,
x2_outdegree_n,
x2_totaldegree_n,
x2_strength,
x2_centr_degree,
x2_a.nn.deg,
x2_a.nn.deg_w) %>% round(3)
#Adding type
x2_df_degree <-cbind(x2_df_degree, V(x2)$LABEL_COR)
#Adding names
names(x2_df_degree) <- c("In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree","Type")
#Ordering Variables
x2_df_degree<-x2_df_degree[c("Type","In Degree", "Out Degree", "Total Degree","In Degree Normalized", "Out Degree Normalized", "Total Degree Normalized", "Strength","Centralization Degree","Average Neighbor Degree","Average Weighted Neighbor Degree")]
datatable(x2_df_degree, filter = 'top')
aggdata_mean <-aggregate(x2_df_degree, by=list(x2_df_degree$Type), FUN=mean, na.rm=TRUE)
#Removing Type variable
aggdata_mean<-aggdata_mean[,-c(2)]
names(aggdata_mean) <- c("Group", "In Degree(M)", "Out Degree(M)", "Total Degree(M)","In Degree Normalized(M)", "Out Degree Normalized(M)", "Total Degree Normalized(M)", "Strength(M)","Centralization Degree(M)","Average Neighbor Degree(M)","Average Weighted Neighbor Degree(M)")
aggdata_sd <-aggregate(x2_df_degree, by=list(x2_df_degree$Type), FUN=sd, na.rm=TRUE)
#Removing Type variable
aggdata_sd<-aggdata_sd[,-c(2)]
names(aggdata_sd) <- c("Group", "In Degree(SD)", "Out Degree(SD)", "Total Degree(SD)","In Degree Normalized(SD)", "Out Degree Normalized(SD)", "Total Degree Normalized(SD)", "Strength(SD)","Centralization Degree(SD)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(SD)")
total <- merge(aggdata_mean,aggdata_sd,by="Group")
#Rounding
Group<-total[,c(1)] #Keeping group
total<-total[,-c(1)] %>% round(2) #Rouding
total<-cbind(Group,total) #Binding toghter
#Organizing Variabels
total<-total[c("Group","In Degree(M)","In Degree(SD)", "Out Degree(M)", "Out Degree(SD)","Total Degree(M)", "Total Degree(SD)", "In Degree Normalized(M)", "In Degree Normalized(SD)", "Out Degree Normalized(M)", "Out Degree Normalized(SD)", "Total Degree Normalized(M)", "Total Degree Normalized(SD)", "Strength(M)","Strength(SD)", "Centralization Degree(M)","Centralization Degree(SD)","Average Neighbor Degree(M)","Average Neighbor Degree(SD)","Average Weighted Neighbor Degree(M)", "Average Weighted Neighbor Degree(SD)")]
datatable(total, filter = 'top')
#Set Seed
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(x2, es=E(x2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(x2))
maxC <- rep(Inf, vcount(x2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(x2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(x2)$weight)
#PLotting
plot(x2,
layout=co,
edge.color=V(x2)$color[edge.start],
edge.arrow.size=(degree(x2)+1)/1000,
edge.width=E(x2)$weight/10,
edge.curved = TRUE,
vertex.size=log((degree(x2)+2))*10,
vertex.size=20,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(x2,"LABEL_COR"),
vertex.label.cex=log((degree(x2)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(x2)$LABEL_COR
b<-V(x2)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=2,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Vertex Degree Sized - 1.2 - ALGUM RELACIONAMENTO (x2)", sub = "Source: from authors ", cex = .5)
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median In Degree: %.2f\n Median Out Degree: %.2f",
median(degree(x2, mode="in")),
median(degree(x2, mode="out"))
))
#Set Seed
set.seed(123)
#Get Variable
V(x2)$x2_color_degree<-V(x2)$totaldegree %>% round(0)
#Creating brewer pallette
vertex_x2_color_degree<-
colorRampPalette(brewer.pal(length(unique(
V(x2)$x2_color_degree)), "RdBu"))(
length(unique(V(x2)$x2_color_degree)))
#Saving as Vertex properties
V(x2)$vertex_x2_color_degree<- vertex_x2_color_degree[as.numeric(cut(degree(x2),breaks =length(unique(V(x2)$x2_color_degree))))]
set.seed(123)
#Plotting based only on degree measures
edge.start <- ends(x2, es=E(x2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(x2))
maxC <- rep(Inf, vcount(x2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(x2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(x2)$weight)
#PLotting
plot(x2,
layout=co,
#edge.color=V(x2)$color[edge.start],
edge.arrow.size=(degree(x2)+1)/1000,
edge.width=E(x2)$weight/10,
edge.curved = TRUE,
vertex.color=V(x2)$vertex_x2_color_degree,
vertex.size=log((degree(x2)+2))*10,
vertex.size=20,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(x2,"LABEL_COR"),
vertex.label.cex=log((degree(x2)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(x2)$x2_color_degree
b<-V(x2)$vertex_x2_color_degree
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
e<-e[order(e$a,decreasing=T),]
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=2,
bty="n",
ncol=1,
lty=1,
cex = .3)
#Adding Title
title("Network Vertex Degree Sized and Red to Blue - 1.2 - ALGUM RELACIONAMENTO (x2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median In Degree: %.2f\nMedian Out Degree: %.2f",
median(degree(x2, mode="in")),
median(degree(x2, mode="out"))
))
#Set Seed
set.seed(123)
#Get Variable
V(x2)$x2_color_degree<-V(x2)$x2_centr_degree
#Creating brewer pallette
vertex_x2_color_degree<-
colorRampPalette(brewer.pal(length(unique(
V(x2)$x2_color_degree)), "Spectral"))(
length(unique(V(x2)$x2_color_degree)))
#Saving as Vertex properties
V(x2)$vertex_x2_color_degree<- vertex_x2_color_degree[as.numeric(cut(V(x2)$x2_color_degree,breaks =length(unique(V(x2)$x2_color_degree))))]
#Plotting based only on degree measures
edge.start <- ends(x2, es=E(x2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(x2))
maxC <- rep(Inf, vcount(x2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(x2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(x2)$weight)
#PLotting
plot(x2,
layout=co,
vertex.color=V(x2)$vertex_x2_color_degree,
edge.color=V(x2)$vertex_x2_color_degree[edge.start],
edge.arrow.size=(degree(x2)+1)/10000,
edge.width=E(x2)$weight/10,
edge.curved = TRUE,
vertex.color=V(x2)$vertex_x2_color_degree,
vertex.size=log((V(x2)$x2_centr_degree+2))*10,
vertex.size=20,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(x2,"LABEL_COR"),
vertex.label.cex=log((degree(x2)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(x2)$x2_color_degree
b<-V(x2)$vertex_x2_color_degree
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
e<-e[order(e$a,decreasing=T),]
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=2,
bty="n",
ncol=1,
lty=1,
cex = .3)
#Adding Title
title("Network Vertex Centralization Degree Sized Spectral Colored - 1.2 - ALGUM RELACIONAMENTO (x2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median In Degree: %.2f\nMedian Out Degree: %.2f",
median(degree(x2, mode="in")),
median(degree(x2, mode="out"))
))
#Set Seed
set.seed(124)
# Network elements with lower than meadian degree
higherthanmedian.network_x2<-V(x2)[degree(x2)<median(degree(x2))]
#Deleting vertices based in intersection betewenn x2
high_x2<-delete.vertices(x2, higherthanmedian.network_x2)
#Plotting based only on degree measures
edge.start <- ends(high_x2, es=E(high_x2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(high_x2))
maxC <- rep(Inf, vcount(high_x2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(high_x2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(high_x2)$weight)
#PLotting
plot(high_x2,
layout=co,
edge.color=V(high_x2)$color[edge.start],
edge.arrow.size=(degree(high_x2)+1)/1000,
edge.width=E(high_x2)$weight/10,
edge.curved = TRUE,
vertex.size=log((V(high_x2)$x2_centr_degree+2))*10,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(high_x2,"LABEL_COR"),
vertex.label.cex=log((degree(high_x2)+2))/10,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(high_x2)$LABEL_COR
b<-V(high_x2)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=3,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Higher Than Median Degree - 1.2 - ALGUM RELACIONAMENTO (x2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Mean In Degree: %.2f\n Mean Out Degree: %.2f",
mean(degree(high_x2, mode="in")),
mean(degree(high_x2, mode="out"))
)
)
#Set Seed
set.seed(123)
# Network elements with lower than meadian degree
lowerthanmedian.network_x2<-V(x2)[degree(x2)>median(degree(x2))]
#Deleting vertices based in intersection betewenn x2
small_x2<-delete.vertices(x2, lowerthanmedian.network_x2)
#Plotting based only on degree measures
edge.start <- ends(small_x2, es=E(small_x2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(small_x2))
maxC <- rep(Inf, vcount(small_x2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(small_x2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(small_x2)$weight)
#PLotting
plot(small_x2,
layout=co,
edge.color=V(small_x2)$color[edge.start],
edge.arrow.size=(degree(small_x2)+1)/1000,
edge.width=E(small_x2)$weight/10,
edge.curved = TRUE,
vertex.size=log((V(small_x2)$x2_centr_degree+2))*20,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(small_x2,"LABEL_COR"),
vertex.label.cex=log((degree(small_x2)+2))/3,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(small_x2)$LABEL_COR
b<-V(small_x2)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=4,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Smaller Than Median Degree - 1.2 - ALGUM RELACIONAMENTO (x2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Mean In Degree: %.2f\nMean Out Degree: %.2f",
mean(degree(small_x2, mode="in")),
mean(degree(small_x2, mode="out"))
)
)
#Set Seed
set.seed(124)
#Plotting based only on degree measures
edge.start <- ends(x2, es=E(x2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(x2))
maxC <- rep(Inf, vcount(x2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(x2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(x2)$weight)
#Plotting based only on degree measures #x2_a.nn.deg
V(x2)$x2_a.nn.deg<-as.numeric(graph.knn(x2)$knn)
V(x2)$x2_a.nn.deg[V(x2)$x2_a.nn.deg=="NaN"]<-0
#PLotting
plot(high_x2,
layout=co,
edge.color=V(x2)$color[edge.start],
edge.arrow.size=sqrt((V(x2)$x2_a.nn.deg)^2+1)/1000,
edge.width=E(x2)$weight/100,
edge.curved = TRUE,
vertex.color=V(x2)$color,
vertex.size=(sqrt((V(x2)$x2_a.nn.deg)^2))/5,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(x2,"LABEL_COR"),
vertex.label.cex=(sqrt((V(x2)$x2_a.nn.deg)^2)+1)/500,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(x2)$LABEL_COR
b<-V(x2)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=4,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Average Neighbor Degree Sized - 1.2 - ALGUM RELACIONAMENTO (x2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median Average Neighbor Degree: %.2f",
median((x2_a.nn.deg+1))
))
#Set Seed
set.seed(124)
#Plotting based only on degree measures
edge.start <- ends(x2, es=E(x2), names=F)[,1]
# Fixing ego
minC <- rep(-Inf, vcount(x2))
maxC <- rep(Inf, vcount(x2))
minC[1] <- maxC[1] <- 0
co <- layout_with_fr(x2, niter=10000, minx=minC, maxx=maxC,miny=minC, maxy=maxC, weights = E(x2)$weight)
#Plotting based only on degree measures #x2_a.nn.deg
V(x2)$x2_a.nn.deg_w<-as.numeric(graph.knn(x2, weights = E(x2)$weight)$knn)
V(x2)$x2_a.nn.deg_w[V(x2)$x2_a.nn.deg_w=="NaN"]<-0
#PLotting
plot(high_x2,
layout=co,
edge.color=V(x2)$color[edge.start],
edge.arrow.size=sqrt((V(x2)$x2_a.nn.deg_w)^2+1)/1000,
edge.width=E(x2)$weight/100,
edge.curved = TRUE,
vertex.color=V(x2)$color,
vertex.size=(sqrt((V(x2)$x2_a.nn.deg_w)^2))/5,
vertex.frame.color="#ffffff",
vertex.label.color="black",
vertex.label=get.vertex.attribute(x2,"LABEL_COR"),
vertex.label.cex=(sqrt((V(x2)$x2_a.nn.deg_w)^2)+1)/500,
vertex.label.dist=0,
rescale=F,
xlim=range(co[,1]),
ylim=range(co[,2]))
axis(1)
axis(2)
#Solving Problems with legend rendering
a<-V(x2)$LABEL_COR
b<-V(x2)$color
c<-table(a,b)
d<-as.data.frame(c)
e<-subset(d, d$Freq>0)
f<-t(e$a)
g<-t(e$b)
#Adding Legend
legend(x=range(co[,1])[2], y=range(co[,2])[2],
legend=as.character(f),
pch=21,
col = "#777777",
pt.bg=as.character(g),
pt.cex=4,
bty="n",
ncol=1,
lty=1,
cex = .5)
#Adding Title
title("Network Average Weighted Neighbor Degree Sized - 1.2 - ALGUM RELACIONAMENTO (x2)", sub = "Source: from authors ")
text(x=range(co[,1])[1], y=range(co[,2])[1], labels =
sprintf("Median Average Weighted Neighbor Degree: %.2f",
median((x2_a.nn.deg_w+1))
))
#Circle Degree ***Too intense computation***
#A_x2 <- get.adjacency(x2, sparse=FALSE)
#detach("package:igraph", unload=TRUE)
#library(network)
#g <- network::as.network.matrix(A_x2)
#library(sna)
#gplot.target(g, degree(g), main="Circle Degree")
#library(igraph)
save.image("~/SNArRDJF/Robject/2_degree_x2.RData")